System level framework for assessing the accuracy of neonatal EEG acquisition
Loading...
Files
Accepted Version
Date
2018-10-29
Authors
O'Sullivan, Mark
Popovici, Emanuel M.
Bocchino, Andrea
O'Mahony, Conor
Boylan, Geraldine B.
Temko, Andriy
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published Version
Abstract
Significant research has been conducted in recent years to design low-cost alternatives to the current EEG monitoring systems used in healthcare facilities. Testing such systems on a vulnerable population such as newborns is complicated due to ethical and regulatory considerations that slow down the technical development. This paper presents and validates a method for quantifying the accuracy of neonatal EEG acquisition systems and electrode technologies via clinical data simulations that do not require neonatal participants. The proposed method uses an extensive neonatal EEG database to simulate analogue signals, which are subsequently passed through electrical models of the skin-electrode interface, which are developed using wet and dry EEG electrode designs. The signal losses in the system are quantified at each stage of the acquisition process for electrode and acquisition board losses. SNR, correlation and noise values were calculated. The results verify that low-cost EEG acquisition systems are capable of obtaining clinical grade EEG. Although dry electrodes result in a significant increase in the skin-electrode impedance, accurate EEG recordings are still achievable.
Description
Keywords
Electrodes , Electroencephalography , Brain modeling , Pediatrics , Impedance , Skin , Monitoring
Citation
O'Sullivan, M., Popovici, E., Bocchino, A., O'Mahony, C., Boylan, G. and Temko, A. (2018) 'System level framework for assessing the accuracy of neonatal EEG acquisition', 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA, 18-21 July, pp. 4339-4342. doi: 10.1109/EMBC.2018.8513246
Link to publisher’s version
Copyright
© 2018, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.